Applying machine learning to anaphora resolution
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
An empirically based system for processing definite descriptions
Computational Linguistics
On coreferring: coreference in MUC and related annotation schemes
Computational Linguistics
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
A corpus-based investigation of definite description use
Computational Linguistics
Corpus-based identification of non-anaphoric noun phrases
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Identifying anaphoric and non-anaphoric noun phrases to improve coreference resolution
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Coreference resolution using competition learning approach
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
High-precision identification of discourse new and unique noun phrases
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Data Mining
A mention-synchronous coreference resolution algorithm based on the Bell tree
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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Given the great amount of definite noun phrases that introduce an entity into the text for the first time, this paper presents a set of linguistic features that can be used to detect this type of definites in Spanish. The efficiency of the different features is tested by building a rule-based and a learning-based chain-starting classifier. Results suggest that the classifier, which achieves high precision at the cost of recall, can be incorporated as either a filter or an additional feature within a coreference resolution system to boost its performance.